Robust Group-Level Inference in Neuroimaging Genetic Studies

Abstract : Gene-neuroimaging studies involve high-dimensional data that have a complex statistical structure and that are likely to be contaminated with outliers. Robust, outlier-resistant methods are an alternative to prior outliers removal, which is a difficult task under high-dimensional unsupervised settings. In this work, we consider robust regression and its application to neuroimaging through an example gene-neuroimaging study on a large cohort of 300 subjects. We use randomized brain parcellation to sample a set of adapted low-dimensional spatial models to analyse the data. We combine this approach with robust regression in an analysis method that we show is outperforming state-of-the-art neuroimaging analysis methods.
Type de document :
Communication dans un congrès
Pattern Recognition in Neuroimaging, Jun 2013, Philadelphie, United States. 2013
Liste complète des métadonnées

https://hal.inria.fr/hal-00833953
Contributeur : Virgile Fritsch <>
Soumis le : jeudi 13 juin 2013 - 17:17:24
Dernière modification le : lundi 4 juin 2018 - 15:42:02
Document(s) archivé(s) le : mardi 4 avril 2017 - 21:47:43

Fichiers

rlm.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00833953, version 1

Collections

Citation

Virgile Fritsch, Benoit Da Mota, Gaël Varoquaux, Vincent Frouin, Eva Loth, et al.. Robust Group-Level Inference in Neuroimaging Genetic Studies. Pattern Recognition in Neuroimaging, Jun 2013, Philadelphie, United States. 2013. 〈hal-00833953〉

Partager

Métriques

Consultations de la notice

533

Téléchargements de fichiers

290